Nowadays, text-to-image generators gained popularity again due to the appearance of diffusion models  [1]}, [2]}, [3]}, [4]}.
Different from GAN where the generator aims to fool the discriminator, the generator of diffusion models tries to reverse the images from noise.
Text-to-image diffusion models take random noise and text as the input and de-noise the noise conditioned on textual guidance.
The process takes a lot of computing resources to cover a variety of different texts.
Currently, the most powerful text-to-image diffusion models, Stable diffusion [2]}, Imagen [4]}, GLIDE [3]} and DALL-E2 [8]}, are published by big companies.
